Pandemic influenza H1N1: reconciling serosurvey data with estimates of the reproduction number

Seasonality as a driver of pH1N12009 influenza vaccination campaign impact

Although the most recent respiratory virus pandemic was triggered by a Coronavirus, sustained and elevated prevalence of highly pathogenic avian influenza viruses able to infect mammalian hosts highlight the continued threat of pandemics of influenza A virus (IAV) to global health. Retrospective analysis of pandemic outcomes, including comparative investigation of intervention efficacy in different regions, provide important contributions to the evidence base for future pandemic planning. The swine-origin IAV pandemic of 2009 exhibited regional variation in onset, infection dynamics and annual infection attack rates (IARs). For example, the UK experienced three severe peaks of infection over two influenza seasons, whilst Australia experienced a single severe wave. We adopt a seasonally forced 2-subtype model for the transmission of pH1N12009 and seasonal H3N2 to examine the role vaccination campaigns may play in explaining differences in pandemic trajectories in temperate regions. Our model differentiates between the nature of vaccine- and infection-acquired immunity. In particular, we assume that immunity triggered by infection elicits heterologous cross-protection against viral shedding in addition to long-lasting neutralising antibody, whereas vaccination induces imperfect reduction in susceptibility. We employ an Approximate Bayesian Computation (ABC) framework to calibrate the model using data for pH1N12009 seroprevalence, relative subtype dominance, and annual IARs for Australia and the UK. Heterologous cross-protection substantially suppressed the pandemic IAR over the posterior, with the strength of protection against onward transmission inversely correlated with the initial reproduction number. We show that IAV pandemic timing relative to the usual seasonal influenza cycle influenced the size of the initial waves of pH1N12009 in temperate regions and the impact of vaccination campaigns.

Interplay between H1N1 influenza a virus infection, extracellular and intracellular respiratory tract pH, and host responses in a mouse model

During influenza A virus (IAV) entry, the hemagglutinin (HA) protein is triggered by endosomal low pH to undergo irreversible structural changes that mediate membrane fusion. HA proteins from different isolates vary in the pH at which they become activated in endosomes or become irreversible inactivated if exposed to extracellular acid. Little is known about extracellular pH in the upper respiratory tracts of mammals, how pH may shift during IAV infection, and its impact on replication of viruses that vary in HA activation pH. Here, we inoculated DBA/2J mice intranasally with A/TN/1-560/2009 (H1N1) (activation pH 5.5) or a mutant containing the destabilizing mutation HA1-Y17H (pH 6.0). We measured the kinetics of extracellular pH during infection using an optical pH-sensitive microsensor probe placed in the naris, nasal sinus, soft palate, and trachea. We also measured intracellular pH of single-cell suspensions of live, primary lung epithelial cells with various wavelength pH-sensitive dyes localized to cell membranes, cytosol, endosomes, secretory vesicles, microtubules, and lysosomes. Infection with either virus decreased extracellular pH and increased intracellular pH. Peak host immune responses were observed at 2 days post infection (DPI) and peak pH changes at 5 DPI. Extracellular and intracellular pH returned to baseline by 7 DPI in mice infected with HA1-Y17H and was restored later in wildtype-infected. Overall, IAV infection altered respiratory tract pH, which in turn modulated replication efficiency. This suggests a virus-host pH feedback loop that may select for IAV strains containing HA proteins of optimal pH stability, which may be approximately pH 5.5 in mice but may differ in other species.

Monitoring the age-specificity of measles transmissions during 2009-2016 in Southern China

Background

Despite several immunization efforts, China saw a resurgence of measles in 2012. Monitoring of transmissions of individuals from different age groups could offer information that would be valuable for planning adequate disease control strategies. We compared the age-specific effective reproductive numbers (R) of measles during 2009–2016 in Guangdong, China.

Methods

We estimated the age-specific R values for 7 age groups: 0–8 months, 9–18 months, 19 months to 6 years, 7–15 years, 16–25 years, 26–45 years, and ≥46 years adapting the contact matrix of China. The daily numbers of laboratory and clinically confirmed cases reported to the Center for Disease Control and Prevention of Guangdong were used.

Results

The peak R values of the entire population were above unity from 2012 to 2016, indicating the persistence of measles in the population. In general, children aged 0–6 years and adults aged 26–45 years had larger values of R when comparing with other age groups after 2012. While the peaks of R values for children aged 0–6 years dropped steadily after 2013, the peaks of R values for adults aged 26–45 years kept at a high range every year.

Conclusions

Although the provincial supplementary immunization activities (SIAs) conducted in 2009 and 2010 were able to reduce the transmissions from 2009 to 2011, larger values of R for children aged 0–6 years were observed after 2012, indicating that the benefits of the SIAs were short-lived. In addition, the transmissions from adults aged between 26 and 45 years increased over time. Disease control strategies should target children and adult groups that carry high potential for measles transmission.

Approximate Bayesian algorithm to estimate the basic reproduction number in an influenza pandemic using arrival times of imported cases

BACKGROUND

In an influenza pandemic, arrival times of cases are a proxy of the epidemic size and disease transmissibility. Because of intense surveillance of travelers from infected countries, detection is more rapid and complete than on local surveillance. Travel information can provide a more reliable estimation of transmission parameters.

METHOD

We developed an Approximate Bayesian Computation algorithm to estimate the basic reproduction number (R₀) in addition to the reporting rate and unobserved epidemic start time, utilizing travel, and routine surveillance data in an influenza pandemic. A simulation was conducted to assess the sampling uncertainty. The estimation approach was further applied to the 2009 influenza A/H1N1 pandemic in Mexico as a case study.

RESULTS

In the simulations, we showed that the estimation approach was valid and reliable in different simulation settings. We also found estimates of R₀ and the reporting rate to be 1.37 (95% Credible Interval [CI]: 1.26-1.42) and 4.9% (95% CI: 0.1%-18%), respectively, in the 2009 influenza pandemic in Mexico, which were robust to variations in the fixed parameters. The estimated R₀ was consistent with that in the literature.

CONCLUSIONS

This method is useful for officials to obtain reliable estimates of disease transmissibility for strategic planning. We suggest that improvements to the flow of reporting for confirmed cases among patients arriving at different countries are required.

A statistical method utilizing information of imported cases to estimate the transmissibility for an influenza pandemic

Background

In a new influenza pandemic, travel data such as arrival times of cases seeded by the originating country can be regarded as a combination of the epidemic size and the mobility networks of infections connecting the originating country with other regions. It can be a complete and timely source for estimating the basic reproduction number (R₀), a key indicator of disease transmissibility.

Method

In this study, we developed a likelihood-based method using arrival times of infected cases in different countries to estimate R₀ for influenza pandemics. A simulation was conducted to assess the performance of the proposed method. We further applied the method to the outbreak of the influenza pandemic A/H1N1 in Mexico.

Results

In the numerical application, the estimated R₀ was equal to 1.69 with a 95% confidence interval (1.65, 1.73). For the simulation results, the estimations were robust to the decline of travel rate and other parameter assumptions. Nevertheless, the estimates were moderately sensitive to the assumption of infectious duration. Generally, the findings were in line with other relevant studies.

Conclusions

Our approach as well as the estimate is potential to assist officials in planning control and prevention measures. Improved coordination to streamline or even centralize surveillance of imported cases among countries will thus be beneficial to public health.

Estimating age-specific reproductive numbers-A comparison of methods

Large outbreaks, such as those caused by influenza, put a strain on resources necessary for their control. In particular, children have been shown to play a key role in influenza transmission during recent outbreaks, and targeted interventions, such as school closures, could positively impact the course of emerging epidemics. As an outbreak is unfolding, it is important to be able to estimate reproductive numbers that incorporate this heterogeneity and to use surveillance data that is routinely collected to more effectively target interventions and obtain an accurate understanding of transmission dynamics. There are a growing number of methods that estimate age-group specific reproductive numbers with limited data that build on methods assuming a homogenously mixing population. In this article, we introduce a new approach that is flexible and improves on many aspects of existing methods. We apply this method to influenza data from two outbreaks, the 2009 H1N1 outbreaks in South Africa and Japan, to estimate age-group specific reproductive numbers and compare it to three other methods that also use existing data from social mixing surveys to quantify contact rates among different age groups. In this exercise, all estimates of the reproductive numbers for children exceeded the critical threshold of one and in most cases exceeded those of adults. We introduce a flexible new method to estimate reproductive numbers that describe heterogeneity in the population.

Transmission of the First Influenza A(H1N1)pdm09 Pandemic Wave in Australia Was Driven by Undetected Infections: Pandemic Response Implications

Background

During the first wave of influenza A(H1N1)pdm09 in Victoria, Australia the rapid increase in notified cases and the high proportion with relatively mild symptoms suggested that community transmission was established before cases were identified. This lead to the hypothesis that those with low-level infections were the main drivers of the pandemic.

Methods

A deterministic susceptible-infected-recovered model was constructed to describe the first pandemic wave in a population structured by disease severity levels of asymptomatic, low-level symptoms, moderate symptoms and severe symptoms requiring hospitalisation. The model incorporated mixing, infectivity and duration of infectiousness parameters to calculate subgroup-specific reproduction numbers for each severity level.

Results

With stratum-specific effective reproduction numbers of 1.82 and 1.32 respectively, those with low-level symptoms, and those with asymptomatic infections were responsible for most of the transmission. The effective reproduction numbers for infections resulting in moderate symptoms and hospitalisation were less than one. Sensitivity analyses confirmed the importance of parameters relating to asymptomatic individuals and those with low-level symptoms.

Conclusions

Transmission of influenza A(H1N1)pdm09 was largely driven by those invisible to the health system. This has implications for control measures–such as distribution of antivirals to cases and contacts and quarantine/isolation–that rely on detection of infected cases. Pandemic plans need to incorporate milder scenarios, with a graded approach to implementation of control measures.

Early real-time estimation of the basic reproduction number of emerging or reemerging infectious diseases in a community with heterogeneous contact pattern: Using data from Hong Kong 2009 H1N1 Pandemic Influenza as an illustrative example

Emerging and re-emerging infections such as SARS (2003) and pandemic H1N1 (2009) have caused concern for public health researchers and policy makers due to the increased burden of these diseases on health care systems. This concern has prompted the use of mathematical models to evaluate strategies to control disease spread, making these models invaluable tools to identify optimal intervention strategies. A particularly important quantity in infectious disease epidemiology is the basic reproduction number, R_0. Estimation of this quantity is crucial for effective control responses in the early phase of an epidemic. In our previous study, an approach for estimating the basic reproduction number in real time was developed. This approach uses case notification data and the structure of potential transmission contacts to accurately estimate R₀ from the limited amount of information available at the early stage of an outbreak. Based on this approach, we extend the existing methodology; the most recent method features intra- and inter-age groups contact heterogeneity. Given the number of newly reported cases at the early stage of the outbreak, with parsimony assumptions on removal distribution and infectivity profile of the diseases, experiments to estimate real time R₀ under different levels of intra- and inter-group contact heterogeneity using two age groups are presented. We show that the new method converges more quickly to the actual value of R₀ than the previous one, in particular when there is high-level intra-group and inter-group contact heterogeneity. With the age specific contact patterns, number of newly reported cases, removal distribution, and information about the natural history of the 2009 pandemic influenza in Hong Kong, we also use the extended model to estimate R₀ and age-specific R₀.

Prior population immunity reduces the expected impact of CTL-inducing vaccines for pandemic influenza control

Vaccines that trigger an influenza-specific cytotoxic T cell (CTL) response may aid pandemic control by limiting the transmission of novel influenza A viruses (IAV). We consider interventions with hypothetical CTL-inducing vaccines in a range of epidemiologically plausible pandemic scenarios. We estimate the achievable reduction in the attack rate, and, by adopting a model linking epidemic progression to the emergence of IAV variants, the opportunity for antigenic drift. We demonstrate that CTL-inducing vaccines have limited utility for modifying population-level outcomes if influenza-specific T cells found widely in adults already suppress transmission and prove difficult to enhance. Administration of CTL-inducing vaccines that are efficacious in "influenza-experienced" and "influenza-naive" hosts can likely slow transmission sufficiently to mitigate a moderate IAV pandemic. However if neutralising cross-reactive antibody to an emerging IAV are common in influenza-experienced hosts, as for the swine-variant H3N2v, boosting CTL immunity may be ineffective at reducing population spread, indicating that CTL-inducing vaccines are best used against novel subtypes such as H7N9. Unless vaccines cannot readily suppress transmission from infected hosts with naive T cell pools, targeting influenza-naive hosts is preferable. Such strategies are of enhanced benefit if naive hosts are typically intensively mixing children and when a subset of experienced hosts have pre-existing neutralising cross-reactive antibody. We show that CTL-inducing vaccination campaigns may have greater power to suppress antigenic drift than previously suggested, and targeting adults may be the optimal strategy to achieve this when the vaccination campaign does not have the power to curtail the attack rate. Our results highlight the need to design interventions based on pre-existing cellular immunity and knowledge of the host determinants of vaccine efficacy, and provide a framework for assessing the performance requirements of high-impact CTL-inducing vaccines.

Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature

BACKGROUND

The potential impact of an influenza pandemic can be assessed by calculating a set of transmissibility parameters, the most important being the reproduction number (R), which is defined as the average number of secondary cases generated per typical infectious case.

METHODS

We conducted a systematic review to summarize published estimates of R for pandemic or seasonal influenza and for novel influenza viruses (e.g. H5N1). We retained and summarized papers that estimated R for pandemic or seasonal influenza or for human infections with novel influenza viruses.

RESULTS

The search yielded 567 papers. Ninety-one papers were retained, and an additional twenty papers were identified from the references of the retained papers. Twenty-four studies reported 51 R values for the 1918 pandemic. The median R value for 1918 was 1.80 (interquartile range [IQR]: 1.47-2.27). Six studies reported seven 1957 pandemic R values. The median R value for 1957 was 1.65 (IQR: 1.53-1.70). Four studies reported seven 1968 pandemic R values. The median R value for 1968 was 1.80 (IQR: 1.56-1.85). Fifty-seven studies reported 78 2009 pandemic R values. The median R value for 2009 was 1.46 (IQR: 1.30-1.70) and was similar across the two waves of illness: 1.46 for the first wave and 1.48 for the second wave. Twenty-four studies reported 47 seasonal epidemic R values. The median R value for seasonal influenza was 1.28 (IQR: 1.19-1.37). Four studies reported six novel influenza R values. Four out of six R values were <1.

CONCLUSIONS

These R values represent the difference between epidemics that are controllable and cause moderate illness and those causing a significant number of illnesses and requiring intensive mitigation strategies to control. Continued monitoring of R during seasonal and novel influenza outbreaks is needed to document its variation before the next pandemic.

The Ratio of Emergency Department Visits for ILI to Seroprevalence of 2009 Pandemic Influenza A (H1N1) Virus Infection, Florida, 2009

BACKGROUND

A seroprevalence survey carried out in four counties in the Tampa Bay area of Florida provided an estimate of cumulative incidence of infection due to the 2009 influenza A (H1N1) as of the end of that year's pandemic in the four counties from which seroprevalence data were obtained Methods. Excess emergency department (ED) visits for influenza-like illness (ILI) during the pandemic period (compared to four non-pandemic years) were estimated using the ESSENCE-FL syndromic surveillance system for the four-county area.

RESULTS

There were an estimated 44 infections for every ILI ED visit. Age-specific ratios rose from 19.7 to 1 for children aged <5 years to 143.8 to 1 for persons aged >64 years.

CONCLUSIONS

These ratios provide a way to estimate cumulative incidence. These estimated ratios can be used in real time for planning and forecasting, when carrying out timely seroprevalence surveys is not practical. Syndromic surveillance data allow age and geographic breakdowns, including for children.

Determining the dynamics of influenza transmission by age

Background

It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa.

Results

Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age.

Conclusions

We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.

Multiple estimates of transmissibility for the 2009 influenza pandemic based on influenza-like-illness data from small US military populations

Rapidly characterizing the amplitude and variability in transmissibility of novel human influenza strains as they emerge is a key public health priority. However, comparison of early estimates of the basic reproduction number during the 2009 pandemic were challenging because of inconsistent data sources and methods. Here, we define and analyze influenza-like-illness (ILI) case data from 2009-2010 for the 50 largest spatially distinct US military installations (military population defined by zip code, MPZ). We used publicly available data from non-military sources to show that patterns of ILI incidence in many of these MPZs closely followed the pattern of their enclosing civilian population. After characterizing the broad patterns of incidence (e.g. single-peak, double-peak), we defined a parsimonious SIR-like model with two possible values for intrinsic transmissibility across three epochs. We fitted the parameters of this model to data from all 50 MPZs, finding them to be reasonably well clustered with a median (mean) value of 1.39 (1.57) and standard deviation of 0.41. An increasing temporal trend in transmissibility ([Formula: see text], p-value: 0.013) during the period of our study was robust to the removal of high transmissibility outliers and to the removal of the smaller 20 MPZs. Our results demonstrate the utility of rapidly available - and consistent - data from multiple populations.

The dynamics of infection and the persistence of immunity to A(H1N1)pdm09 virus in Israel

BACKGROUND

Influenza virus A(H1N1)pdm09 first appeared in Israel in late April 2009, disappeared in mid-March 2010, and reappeared in late October 2010. Symptoms were mostly mild without need for medical care.

OBJECTIVES

To provide targets for future pandemic preparedness and response by evaluating the dynamics and cumulative incidence of A(H1N1)pdm09 infection, the virus-specific seroprevalence (HI antibody titer >1:40) at the height of the pandemic, during its decline and thereafter.

METHODS

A cross-sectional seroepidemiological study was conducted on 6911 serum samples collected before, during, and after the pandemic.

RESULTS

Cumulative incidence of infection derived from the differences between post- and pre-pandemic seroprevalence was 54.1%, 32.9%, 22.9%, 14.8%, and 6.3% in age-groups 0-9, 10-19, 20-49, 50-79, and ≥ 80 years, respectively, and 28.5% for all age-groups combined. Vaccination could have contributed at the most 4.6% to the post-pandemic population seroprevalence. High pre-pandemic immune response (47.4%) found in a cohort aged 15-18 year was strongly associated with birth years 1990-1993. Morbidity began to decline in mid-November 2009 at 32.8% population seroprevalence (45% in ages 0-19 year) and stopped in March 2010 at 43.4% population seroprevalence in February 2010 (70% in ages 0-19 year). Between February and September 2010, seroprevalence declined by 12.2% allowing virus recirculation from October 2010.

CONCLUSIONS

Our study provides targets for controlling future influenza pandemics in Israel. Vaccination should focus on the younger age-groups (0-19 year) which played a key role in transmission of the A(H1N1)pdm09 due to lack of background immunity (ages 0-9 year) and high exposure rates (ages 10-19 year).